John Heine

This paper compares total x-ray energy absorbed by fibroglandular tissue and its mass from FFDM and DBT, finding strong linear associations with minimal need for quadratic modeling.

This paper evaluates synthetic data generation methods using random projection and multivariate similarity assessment in low-dimensional settings to address small-sample challenges in biomedical research.

This paper develops an automated breast density detection technique that improves risk prediction by synthesizing a signal-dependent noise stochastic process across different mammographic technologies.

The paper evaluates a synthetic data generation technique to address the small sample problem by creating statistically similar synthetic populations.

This paper identifies genetic loci associated with mammographic texture variation and finds significant genetic correlations with breast cancer risk and mammographic density.

This paper introduces a method to generate synthetic populations from sparse biomedical data, showing they are statistically similar to observed samples and can help overcome small sample size limitations.